Neighbour Links Travel Time Estimation Using Probe Vehicles and Buses Data
Bibliographic record
Abstract
The importance of delivering real-time traffic information to travelers is undeniable. One cost efficient approach for real-time travel time data collection is using Vehicles As Probes (VAP) “moving sensors”. Probe vehicles are usually regarded as passive vehicles as they do not exist on the network for data collection. Travel time estimation using probe vehicles data is usually limited to their travel routes. In this research, a framework is presented for travel time estimation on a road network using sparse travel time data. The purpose is to estimate travel times on links not covered with sensors by using their travel time relationships with neighbor links. A case study is applied to the road network of downtown Vancouver using a VISSIM micro-simulation model. Three different market penetration levels of probe vehicles were tested: 1%, 3%, and 5%. The estimation accuracy was assessed by the Mean Absolute Percentage Error (MAPE), the value of which, ranged between 12.7% and 16.2% for the three tested market penetration levels. The potential of fusing buses travel time data and passenger probes data to estimate travel times of neighbor links was investigated.The paper shows that using transit data for neighbor links travel time estimation can improve the accuracy of estimation at low market penetration levels. The significance of transit data diminishes with the increase of passenger probes market penetration level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".